In recent years, computer systems have been afforded a greater complexity and scale by interconnecting various information processing devices via a computer network (referred to hereinbelow simply as a “network”). As a result, a fault generated in any one information processing device now affects various information processing devices via the network.
Conventionally, root cause parsing techniques for specifying the site and cause of such a fault have included the event correlation technique with which the site and cause of the fault are parsed using an event indicating fault content that is transmitted from the information processing device (see PTL1, for example). The event correlation technique infers the root cause by using an event correlation that is established on the management server at the time of the fault, and has therefore long been used in the diagnosis of a network system fault.
Moreover, non-PTL1 discloses this technique and a technique for rapidly determining the root cause using an inference engine based on an expert system by creating a rule by pairing a combination of events at the time of a fault with an inferred root cause.
In this expert system-based event correlation, when there is an increase in the scale of the IT system that is to undergo fault parsing or a large number of rules, a large working memory size is required for the rule parsing network linking the relationships between the rules used in the pattern matching of the inference engine of PTL2. One such rule parsing network is a technique called a codebook that is disclosed in PTL2 and with which the relationship between the event when the fault occurs and what is considered the root cause is expressed in a matrix format referred to as a ‘matrix’ and with which technique partitioning and minimization are carried out by optimizing the matrix.